AI red teaming is the practice of simulating a determined adversary against an AI system to discover how it can be made to fail before someone makes it fail in production. Red teamers attack the model, the application around it, the tools it can use, and the guardrails meant to contain it, pursuing real objectives the way an attacker would: extracting data, bypassing safety controls, or turning the system's own capabilities against its owner. It has moved quickly from a research practice at frontier labs to a mainstream requirement for any organization shipping AI products, and it is increasingly expected by regulators and enterprise buyers alike.
The term borrows from traditional security red teaming, where a team emulates real adversaries to test not just whether controls exist but whether they hold. Applied to AI, the idea is the same, but the attack surface is different: instead of exploiting code and configuration, the red team exploits how the model interprets language, how it uses tools, and how its guardrails behave under pressure. This article explains what AI red teaming involves, how it differs from AI penetration testing, the techniques it uses, and how to run it so the results actually improve your security. We deliver this work through our AI security service.
What is AI red teaming?
AI red teaming is structured, adversarial testing of an AI system against realistic objectives. Rather than checking a list of known issues, the red team behaves like an attacker who wants a specific outcome and will combine techniques to get there. That might mean chaining an indirect prompt injection in a retrieved document with an over-permissioned tool to exfiltrate data, or wearing down a guardrail through a sequence of reformulated requests. The output is not just a list of weaknesses but an account of what a capable adversary could actually achieve.
Crucially, AI red teaming tests the system as a whole, including detection and response. It asks not only whether an attack succeeds but whether anyone would notice, and whether the controls that are supposed to contain the model actually do so when pushed. That systemic view is what separates red teaming from a narrower, scoped assessment.
AI red teaming vs AI penetration testing
These terms overlap and are often used loosely, but the distinction is useful. AI penetration testing is typically a scoped assessment of a specific application, focused on finding and proving concrete vulnerabilities within defined targets. AI red teaming is broader and goal-driven: it simulates a real adversary across the whole system and tests the organization's ability to detect and respond, not just the controls themselves. We cover the scoped assessment in AI penetration testing: how to test LLM apps, agents, and RAG.
In practice, a penetration test answers what is exploitable here, while red teaming answers how far a determined adversary could get and whether you would see it coming. Organizations often begin with penetration testing and adopt red teaming as their AI systems become more capable and more central to the business.
Why AI red teaming matters now
Three shifts have made AI red teaming urgent rather than optional.
- AI systems increasingly take actions, not just generate text, so a manipulated model can move money, change data, or send communications.
- The attack surface is novel and poorly understood, so controls that look solid often fail the first time a skilled adversary pushes on them.
- Regulation and procurement now expect it: the EU AI Act requires robustness and security for high-risk systems, and enterprise buyers ask how AI products have been tested.
Guardrails that hold in a demo are not guardrails. Red teaming exists to find out what your AI does under a determined adversary, not a cooperative user.
What AI red teaming targets
A thorough red team engagement reaches across the whole AI system, because the most damaging attacks usually combine weaknesses in different components.
- The model's behavior, including jailbreaks, harmful output, and the limits of its safety training.
- Prompt injection, both direct from the user and indirect through data the model retrieves or processes.
- Agents and tool use, where the red team tests what an attacker can make the system do once it is manipulated.
- Retrieval pipelines (RAG), tested for data leakage, poisoning, and context manipulation.
- Guardrails and filters, tested under sustained adversarial pressure rather than in their intended use.
- Detection and response, to establish whether the organization would notice and contain an attack in progress.
Techniques AI red teams use
Red teamers draw on a growing catalogue of techniques, many catalogued in frameworks such as MITRE ATLAS and the OWASP Top 10 for LLM Applications. Common approaches include the following.
- Direct prompt injection, instructing the model to ignore its constraints or reveal its system prompt.
- Indirect prompt injection, planting instructions in content the model will later retrieve, such as a document, webpage, or email.
- Jailbreak chains, using role-play, reformulation, or encoding to bypass safety controls a single prompt would not.
- Tool and function abuse, steering the model's available actions toward unintended or harmful outcomes.
- Data extraction, probing for training data, secrets, or information belonging to other users or contexts.
- Multi-step objectives, combining several weaknesses into a realistic attack path rather than isolated findings.
How an AI red team engagement runs
A well-run engagement is structured, not ad hoc, and produces results an organization can act on.
- Objectives and rules, where we agree the adversary profile, the goals to pursue, and the limits, in writing.
- Threat modeling, where we map the system and identify the most consequential objectives a real attacker would pursue.
- Execution, where the red team pursues those objectives across the system, combining techniques as an attacker would.
- Evidence, where every successful attack is documented with reproducible steps and a clear business impact.
- Reporting, where findings are prioritized and paired with concrete guardrail and architecture recommendations.
- Retesting, where we confirm that the defenses put in place actually withstand the attacks that previously worked.
Turning red team findings into stronger defenses
Red teaming has value only if its findings change the system. Because most serious AI weaknesses are structural rather than cosmetic, the durable fixes sit in architecture and controls, not in wording. Scoped tool permissions, hard boundaries the model cannot cross, validation of model output before downstream systems trust it, and complete logging are the kinds of controls that hold. We design and validate these guardrails as part of the engagement, and the broader approach is described on our AI security service page.
Detection matters as much as prevention. Because no guardrail is perfect, the ability to notice an attack in progress and respond is part of the defense, which we cover through detection and response. Red teaming that improves both prevention and detection is what actually reduces risk.
AI red teaming and regulation
Red teaming is increasingly tied to compliance. The EU AI Act requires high-risk AI systems to be accurate, robust, and secure, and adversarial testing is how those properties are demonstrated. The NIST AI Risk Management Framework, which we explain in the NIST AI RMF guide, treats testing and measurement as central to managing AI risk. Aligning red teaming to these frameworks means the same work supports security and regulatory evidence together, as set out on our EU AI Act page.
AI systems fail in ways traditional testing does not catch, and the only reliable way to know how yours fails is to attack it on purpose. If you are putting models, agents, or RAG pipelines into production, see our AI security service and book a scoping call to discuss a red team engagement.